3807708: CF_Big Data Analytics

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Semester:WS 14/15
Scheduled in semester:1-6
Semester Hours per Week / Contact Hours:30.0 L / 22.5 h
Self-directed study time:67.5 h

Module coordination/Lecturers


Bachelor's degree programme in Business Administration (01.09.2012)
Master's degree programme in Architecture (01.09.2014)
Bachelor's degree programme in Architecture (01.09.2014)


  • Introduction into "Big Data Analytics"
  • Gathering Data with Python
  • Cleaning Up Data with Python
  • Interpreting Data with R
  • Predictive Models with R

Learning Outcomes

  • Repeat the fundamental concepts and definitions in the area of big data analytics
  • Understand the benefits of using and interpreting large data sets derived from various data sources.
  • Solve assignments, especially case studies in the area of smart - Identify relationships between different types of data.
  • Describe data an build prediction models.
  • Compare solutions with regard to their prediction accuracy.
  • Evaluation and select suitable prediction models.


Lectures Method

  • Lecture with interactive elements
  • Team project work

Admission Requirements

  • Basic programming skills
  • Basic skills of descriptive statistics


Field, A. P., Miles, J., & Field, Z. (2012). Discovering statistics using R. London: Sage.
Chang, W. (2013). R graphics cookbook. Beijing: O'Reilly.
Downey, A. (2013): Think Python - How to Think Like a Computer Scientist. Green Tea Press.


Will be provided in class


Passed / failed

Assessment tasks:
Part A: 50 % Mid term presentation
Part B: 50 % Final presentation


Cross-faculty elective subject:
Notice the special Multi-stage allocation process.


  • P-FU_Big Data Analytics (WS 14/15, in Bewertung)